From multivariate to functional data analysis: Fundamentals, recent developments, and emerging areas

被引：10

作者：

Li, Yehua ^{[1
]}

Qiu, Yumou ^{[2
]}

Xu, Yuhang ^{[3
]}

机构：

[1] Univ Calif Riverside, Riverside, CA 92521 USA

[2] Iowa State Univ, Ames, IA 50011 USA

[3] Bowling Green State Univ, Bowling Green, OH 43403 USA

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2022年 / 188卷

基金：

美国国家卫生研究院;

关键词：

Functional data analysis; High-dimensional statistics; Multi-level modeling; Spatial dependence; PRINCIPAL COMPONENT ANALYSIS; VARYING COEFFICIENT MODELS; SPATIOTEMPORAL POINT-PROCESSES; GENERALIZED LINEAR-MODELS; LIKELIHOOD RATIO TESTS; VARIABLE SELECTION; SPLINE MODELS; SPARSE; REGRESSION; STATISTICS;

D O I：

10.1016/j.jmva.2021.104806

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Functional data analysis (FDA), which is a branch of statistics on modeling infinite dimensional random vectors resided in functional spaces, has become a major research area for Journal of Multivariate Analysis. We review some fundamental concepts of FDA, their origins and connections from multivariate analysis, and some of its recent developments, including multi-level functional data analysis, high-dimensional functional regression, and dependent functional data analysis. We also discuss the impact of these new methodology developments on genetics, plant science, wearable device data analysis, image data analysis, and business analytics. Two real data examples are provided to motivate our discussions. (C) 2021 Elsevier Inc. All rights reserved.

引用

页数：15

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